This places the robotic free-flying motion planning problem in the context of motion planning under parametric uncertainty. Existing planning under parametric uncertainty approaches are wide-ranging, but can be broadly placed into two categories. Some approaches attempt full system identification (sys ID) before even attempting motion planning [1]}, [2]}, [3]} followed by planning with the estimated nominal model; otherwise, robust or chance-constrained approaches that operate under an assumed uncertainty bound [4]} [5]} [6]} are applied. Many approaches from robust and adaptive control can be applied to the uncertain tracking problem, but do not address the higher-level motion planning. Direct adaptive control [7]} and sliding mode control can be employed to reject disturbances from parametric model uncertainty [8]}, [9]}, [10]}. Indirect adaptive control approaches on the other hand, pair an estimator and a controller, relying on the estimator to provide a better model for the control method under the condition of a persistently exciting control signal [11]}, [12]}. Providing robustness against assumed uncertainty bounds neglects the higher-level motion planning problem, and does not consider online reduction of uncertainty. For learnable robotic parametric unknowns, replanning capability is desirable due to an evolving understanding of the unknown parameters; further, excitation might be desired in order to aid in this model-learning process.
